issues: 398107776
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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type |
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| 398107776 | MDU6SXNzdWUzOTgxMDc3NzY= | 2666 | Dataset.from_dataframe will produce a FutureWarning for DatetimeTZ data | 1217238 | open | 0 | 6 | 2019-01-11T02:45:49Z | 2019-12-30T22:58:23Z | MEMBER | This appears with the development version of pandas; see https://github.com/pandas-dev/pandas/issues/24716 for details. Example: ``` In [16]: df = pd.DataFrame({"A": pd.date_range('2000', periods=12, tz='US/Central')}) In [17]: df.to_xarray() /Users/taugspurger/Envs/pandas-dev/lib/python3.7/site-packages/xarray/core/dataset.py:3111: FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct 'tz'. To accept the future behavior, pass 'dtype=object'. To keep the old behavior, pass 'dtype="datetime64[ns]"'. data = np.asarray(series).reshape(shape) Out[17]: <xarray.Dataset> Dimensions: (index: 12) Coordinates: * index (index) int64 0 1 2 3 4 5 6 7 8 9 10 11 Data variables: A (index) datetime64[ns] 2000-01-01T06:00:00 ... 2000-01-12T06:00:00 ``` |
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13221727 | issue |